Rolling horizon policies for multi-stage stochastic assemble-to-order problems

TL;DR

This paper explores rolling horizon policies for multi-stage stochastic assemble-to-order problems, enhancing two-stage models with terminal value functions to better handle non-stationary, correlated, and seasonal demand in production planning.

Contribution

It introduces a piecewise linear approximation of terminal inventory value to improve two-stage stochastic models for assemble-to-order systems under complex demand patterns.

Findings

Adding terminal value functions improves out-of-sample performance.

Demand correlations and capacity levels influence model effectiveness.

The approach supports typical MRP/ERP systems with two-level planning.

Abstract

Assemble-to-order approaches deal with randomness in demand for end items by producing components under uncertainty, but assembling them only after demand is observed. Such planning problems can be tackled by stochastic programming, but true multistage models are computationally challenging and only a few studies apply them to production planning. Solutions based on two-stage models are often short-sighted and unable to effectively deal with non-stationary demand. A further complication may be the scarcity of available data, especially in the case of correlated and seasonal demand. In this paper, we compare different scenario tree structures. In particular, we enrich a two-stage formulation by introducing a piecewise linear approximation of the value of the terminal inventory, to mitigate the two-stage myopic behavior. We compare the out-of-sample performance of the resulting models by rolling horizon simulations, within a data-driven setting, characterized by seasonality, bimodality, and correlations in the distribution of end item demand. Computational experiments suggest the potential benefit of adding a terminal value function and illustrate interesting patterns arising from demand correlations and the level of available capacity. The proposed approach can provide support to typical MRP/ERP systems, when a two-level approach is pursued, based on master production and final assembly scheduling.